Quantum-enhanced deliberation of learning agents using trapped ions

Understand

A scheme that successfully employs quantum mechanics in the design of autonomous learning agents has recently been reported in the context of the projective simulation (PS) model for artificial intelligence.

• In that approach, the key feature of a PS agent, a specific type of memory which is explored via random walks, was shown to be amenable to quantization.
• In particular, classical random walks were substituted by Szegedy-type quantum walks, allowing for a speed-up.
• In this work we propose how such classical and quantum agents can be implemented in systems of trapped ions.